Examples with SLProblem edu.illinois.cs.cogcomp.sl.core.SLProblem used on opensource projects

Example 1 with SLProblem

use of edu.illinois.cs.cogcomp.sl.core.SLProblem in project cogcomp-nlp by CogComp.

the class CommaIOManager method readProblem.

public static SLProblem readProblem(List<CommaSRLSentence> sentences, Lexiconer lexicon, List<Classifier> lbjExtractors, Classifier lbjLabeler) {
    if (lexicon.isAllowNewFeatures())
        lexicon.addFeature(unknownFeature);
    // lexicon.addLabel("occupy-zero-label-for-some-reason");
    SLProblem sp = new SLProblem();
    // READ PROBLEM
    for (CommaSRLSentence sentence : sentences) {
        List<CommaSequence> commaSequences = getCommaSequences(sentence, lexicon, lbjExtractors);
        for (CommaSequence commaSequence : commaSequences) {
            CommaLabelSequence labelSequence = new CommaLabelSequence(commaSequence, lexicon, lbjLabeler);
            sp.addExample(commaSequence, labelSequence);
        }
    }
    return sp;
}

Example 2 with SLProblem

use of edu.illinois.cs.cogcomp.sl.core.SLProblem in project cogcomp-nlp by CogComp.

the class StructuredCommaClassifier method test.

/**
     * 
     * @param sentences the test set
     * @param predictionFileName location to which to save the predictions of the model. If it is
     *        null, predictions are not saved
     * @return and EvaluateDiscrete object which can provide the performance statistics
     * @throws Exception
     */
public EvaluateDiscrete test(List<CommaSRLSentence> sentences, String predictionFileName) throws Exception {
    lm.setAllowNewFeatures(false);
    SLProblem sp = CommaIOManager.readProblem(sentences, lm, lbjExtractors, lbjLabeler);
    EvaluateDiscrete SLEvaluator = new EvaluateDiscrete();
    BufferedWriter writer = null;
    if (predictionFileName != null) {
        writer = new BufferedWriter(new FileWriter(predictionFileName));
    }
    for (int i = 0; i < sp.instanceList.size(); i++) {
        CommaLabelSequence gold = (CommaLabelSequence) sp.goldStructureList.get(i);
        CommaLabelSequence prediction = (CommaLabelSequence) infSolver.getBestStructure(wv, sp.instanceList.get(i));
        for (int j = 0; j < prediction.labels.size(); j++) {
            String predictedTag = prediction.labels.get(j);
            String goldTag = gold.labels.get(j);
            SLEvaluator.reportPrediction(predictedTag, goldTag);
        }
        if (predictionFileName != null) {
            CommaSequence instance = ((CommaSequence) sp.instanceList.get(i));
            instance.sortedCommas.get(i).getSentence().getAnnotatedText();
            for (int j = 0; j < prediction.labels.size(); j++) {
                int commaPosition = instance.sortedCommas.get(j).commaPosition;
                String predictedLabel = lm.getLabelString(Integer.parseInt((prediction.labels.get(j))));
                writer.write(commaPosition + "\t" + predictedLabel + "\n");
            }
            writer.write("\n");
        }
    }
    if (predictionFileName != null) {
        writer.close();
    }
    return SLEvaluator;
}

Example 3 with SLProblem

use of edu.illinois.cs.cogcomp.sl.core.SLProblem in project cogcomp-nlp by CogComp.

the class StructuredCommaClassifier method train.

/**
     * 
     * @param sentences the training set
     * @param modelPath the location to save the learnt model. If it is null, it is not saved
     * @throws Exception
     */
public void train(List<CommaSRLSentence> sentences, String modelPath) throws Exception {
    lm.setAllowNewFeatures(true);
    SLProblem sp = CommaIOManager.readProblem(sentences, lm, lbjExtractors, lbjLabeler);
    // numLabels*numLabels for transition features
    // numWordsInVocab*numLabels for emission features
    // numLabels for prior on labels
    int numFeatures = lm.getNumOfFeature();
    int numLabels = lm.getNumOfLabels();
    para.TOTAL_NUMBER_FEATURE = numFeatures * numLabels + numLabels + numLabels * numLabels;
    Learner learner = LearnerFactory.getLearner(infSolver, featureGenerator, para);
    wv = learner.train(sp);
    // save the model
    if (modelPath != null)
        saveModel(modelPath);
}